Zobrazeno 1 - 10
of 3 501
pro vyhledávání: '"Chen , Zhenyu"'
Autor:
Han, Tingxu, Sun, Weisong, Hu, Yanrong, Fang, Chunrong, Zhang, Yonglong, Ma, Shiqing, Zheng, Tao, Chen, Zhenyu, Wang, Zhenting
Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potential for these models to create content that infringes on copyrights or
Externí odkaz:
http://arxiv.org/abs/2412.00580
Autor:
Chen, Yuchen, Sun, Weisong, Fang, Chunrong, Chen, Zhenpeng, Ge, Yifei, Han, Tingxu, Zhang, Quanjun, Liu, Yang, Chen, Zhenyu, Xu, Baowen
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these models are susceptible to security vulnerabilities, drawing increasin
Externí odkaz:
http://arxiv.org/abs/2410.15631
Autor:
Ge, Yifei, Sun, Weisong, Lou, Yihang, Fang, Chunrong, Zhang, Yiran, Li, Yiming, Zhang, Xiaofang, Liu, Yang, Zhao, Zhihong, Chen, Zhenyu
Recent advancements in large language models (LLMs) have revolutionized code intelligence by improving programming productivity and alleviating challenges faced by software developers. To further improve the performance of LLMs on specific code intel
Externí odkaz:
http://arxiv.org/abs/2410.02841
Software testing is a crucial phase in the software life cycle, helping identify potential risks and reduce maintenance costs. With the advancement of Large Language Models (LLMs), researchers have proposed an increasing number of LLM-based software
Externí odkaz:
http://arxiv.org/abs/2409.17561
In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks play a crucial role in the development and deployment. It is essential to
Externí odkaz:
http://arxiv.org/abs/2409.14968
Regarding software engineering (SE) tasks, Large language models (LLMs) have the capability of zero-shot learning, which does not require training or fine-tuning, unlike pre-trained models (PTMs). However, LLMs are primarily designed for natural lang
Externí odkaz:
http://arxiv.org/abs/2409.14644
Autor:
Guo, An, Zhou, Yuan, Tian, Haoxiang, Fang, Chunrong, Sun, Yunjian, Sun, Weisong, Gao, Xinyu, Luu, Anh Tuan, Liu, Yang, Chen, Zhenyu
Publikováno v:
39th IEEE/ACM International Conference on Automated Software Engineering (ASE '24), October 27-November 1, 2024, Sacramento, CA, USA
Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not
Externí odkaz:
http://arxiv.org/abs/2409.08081
Automatic programming attempts to minimize human intervention in the generation of executable code, and has been a long-standing challenge in the software engineering community. To advance automatic programming, researchers are focusing on three prim
Externí odkaz:
http://arxiv.org/abs/2409.03267
Autor:
Guo, An, Gao, Xinyu, Chen, Zhenyu, Xiao, Yuan, Liu, Jiakai, Ge, Xiuting, Sun, Weisong, Fang, Chunrong
Publikováno v:
Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA '24), September 16--20, 2024, Vienna, Austria
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) collaboration has the potential t
Externí odkaz:
http://arxiv.org/abs/2408.16470
Autor:
Sun, Weisong, Chen, Yuchen, Fang, Chunrong, Feng, Yebo, Xiao, Yuan, Guo, An, Zhang, Quanjun, Liu, Yang, Xu, Baowen, Chen, Zhenyu
Neural code models (NCMs) have been widely used for addressing various code understanding tasks, such as defect detection and clone detection. However, numerous recent studies reveal that such models are vulnerable to backdoor attacks. Backdoored NCM
Externí odkaz:
http://arxiv.org/abs/2408.04683